Neural Networks in Software and Hardware Testing
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Back to Chuck
Anderson's Home Page
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Chuck Anderson, CS faculty member,
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Anneliese von
Mayrhauser, CS faculty member,
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Tom
Chen, EE faculty member,
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Lei Shi, CS graduate
student,
- Ruiying Zhang, CS graduate
- Uli Koepping, CS graduate
- Craig Gideon, CS
graduate student,
- Amjad Hajjar, EE graduate student,
- Andre Bi, EE undergraduate student,
Past members:
- Rick Mraz, PhD graduate in CS
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This work has been funded by NSF grants
- National Science Foundation, MIP-9628770, 8/96--7/99, $314,313,
with A. von Mayrhauser, Computer Science, and T. Chen, Electrical Engineering,
Behavioral Level Design Verifications Using Software
Testing Techniques and Neural Networks
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A difficult step in the testing of software or hardware is the choice of test
cases to probe the behavior of the system. Anneliese von Mayrhauser, Rick Mraz, and I show how a neural network
trained to predict software faults can be inverted to predict additional test
cases that are likely to produce additional faults:
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von Mayrhauser, A., Anderson, C.W., Chen, T., Mraz, R., and Gideon,
C.A. (1998). On the Promise of Neural Networks to Support Software
Testing. In Computational Intelligence in Software
Engineering, ed. by W. Pedrycz and J.F. Peters, World Scientific,
pp. 3--32.
- C. Anderson, A. von Mayrhauser, and R. Mraz.
On the Use of Neural Networks to Guide Software Testing
Activities.
Proceedings of ITC'95, the International Test Conference,
October 21--26, 1995, Washington, D.C. (59 KB compressed postscript)
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With Tom Chen,
we have also applied the same technique to generate test stimuli to VHDL
models of hardware designs:
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Neural Networks in Software and Hardware Testing Research in CS at CSU, Charles
W. Anderson /
anderson@cs.colostate.edu
Copyright © 1998 Charles Anderson